{"title":"c-JRefRec: Change-based identification of Move Method refactoring opportunities","authors":"Naoya Ujihara, Ali Ouni, T. Ishio, Katsuro Inoue","doi":"10.1109/SANER.2017.7884658","DOIUrl":null,"url":null,"abstract":"We propose, in this paper, a lightweight refactoring recommendation tool, namely c-JRefRec, to identify Move Method refactoring opportunities based on four heuristics using static and semantic program analysis. Our tool aims at identiying refactoring opportunities before a code change is committed to the codebase based on current code changes whenever the developer saves/compiles his code. We evaluate the efficiency of our approach in detecting Feature Envy smells and recommending Move Method refactorings to fix them on three Java open-source systems and 30 code changes. Results show that our approach achieves an average precision of 0.48 and 0.73 of recall and outperforms a state-of-the-art approach namely JDeodorant.","PeriodicalId":6541,"journal":{"name":"2017 IEEE 24th International Conference on Software Analysis, Evolution and Reengineering (SANER)","volume":"27 1","pages":"482-486"},"PeriodicalIF":0.0000,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE 24th International Conference on Software Analysis, Evolution and Reengineering (SANER)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SANER.2017.7884658","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 13
Abstract
We propose, in this paper, a lightweight refactoring recommendation tool, namely c-JRefRec, to identify Move Method refactoring opportunities based on four heuristics using static and semantic program analysis. Our tool aims at identiying refactoring opportunities before a code change is committed to the codebase based on current code changes whenever the developer saves/compiles his code. We evaluate the efficiency of our approach in detecting Feature Envy smells and recommending Move Method refactorings to fix them on three Java open-source systems and 30 code changes. Results show that our approach achieves an average precision of 0.48 and 0.73 of recall and outperforms a state-of-the-art approach namely JDeodorant.